The model takes 1-minute OHLCV and Orderbook data as input and learns temporal patterns through a deep learning network combining CNN and LSTM architectures. The reinforcement learning agent makes buy/sell/hold decisions based on processed features, continuously optimizing trading strategies through rewards received from the market environment.

1-Minute OHLCV Data: Utilizes Open, High, Low, Close, and Volume data at 1-minute intervals for comprehensive price action analysis

1-Minute Orderbook Data: Incorporates real-time orderbook depth information to capture market microstructure and liquidity dynamics

Please use the provided environment.yml file to set up the required dependencies:

conda env create -f environment.yml
conda activate rl-hft

src/agent/python ppo_agent_tech.py --include_tech True --input_type LSTM src/agent/python sac_agent_tech.py --include_tech True --input_type LSTM